




'''simple docstring'''







import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DetrImageProcessor

class 	_snake_case(			unittest.TestCase      ):






 def __init__(self       :  Optional[Any]    ,		a       :  List[str]    ,		a       :  Tuple=7    ,		a       :  Optional[Any]=3    ,		a       :  Optional[int]=30    ,		a       :  Optional[Any]=4_00    ,		a       :  Union[str, Any]=True    ,		a       :  Any=None    ,		a       :  int=True    ,		a       :  List[Any]=1 / 2_55    ,		a       :  List[Any]=True    ,		a       :  Dict=[0.5, 0.5, 0.5]    ,		a       :  str=[0.5, 0.5, 0.5]    ,		a       :  Dict=True    ,		)       ->   List[Any]:






    """simple docstring"""


    A__       =     size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
    A__       =     parent
    A__       =     batch_size
    A__       =     num_channels
    A__       =     min_resolution
    A__       =     max_resolution
    A__       =     do_resize
    A__       =     size
    A__       =     do_rescale
    A__       =     rescale_factor
    A__       =     do_normalize
    A__       =     image_mean
    A__       =     image_std
    A__       =     do_pad






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Optional[Any]:






    """simple docstring"""


    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_rescale": self.do_rescale,
        "rescale_factor": self.rescale_factor,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_pad": self.do_pad,
    }






 def 						_UpperCamelCase (self       :  Dict    ,		a       :  List[Any]    ,		a       :  Any=False    )       ->   int:






    """simple docstring"""


    if not batched:
       A__       =     image_inputs[0]
       if isinstance(a    ,		Image.Image    ):
          A__  ,			A__       =     image.size
       else:
          A__  ,			A__       =     image.shape[1], image.shape[2]
       if w < h:
          A__       =     int(self.size['shortest_edge'] * h / w    )
          A__       =     self.size['shortest_edge']
       elif w > h:
          A__       =     self.size['shortest_edge']
          A__       =     int(self.size['shortest_edge'] * w / h    )
       else:
          A__       =     self.size['shortest_edge']
          A__       =     self.size['shortest_edge']

    else:
       A__       =     []
       for image in image_inputs:
          A__  ,			A__       =     self.get_expected_values([image]    )
          expected_values.append((expected_height, expected_width)    )
       A__       =     max(a    ,		key=lambda a    : item[0]    )[0]
       A__       =     max(a    ,		key=lambda a    : item[1]    )[1]

    return expected_height, expected_width




@require_torch
@require_vision
class 	_snake_case(			UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							List[Any]						=					DetrImageProcessor if is_vision_available() else None






 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[str]:






    """simple docstring"""


    A__       =     DetrImageProcessingTester(self    )






 @property
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Dict:






    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Dict:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    self.assertTrue(hasattr(a    ,		'image_mean'    )    )
    self.assertTrue(hasattr(a    ,		'image_std'    )    )
    self.assertTrue(hasattr(a    ,		'do_normalize'    )    )
    self.assertTrue(hasattr(a    ,		'do_rescale'    )    )
    self.assertTrue(hasattr(a    ,		'rescale_factor'    )    )
    self.assertTrue(hasattr(a    ,		'do_resize'    )    )
    self.assertTrue(hasattr(a    ,		'size'    )    )
    self.assertTrue(hasattr(a    ,		'do_pad'    )    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Dict:






    """simple docstring"""


    A__       =     self.image_processing_class.from_dict(self.image_processor_dict    )
    self.assertEqual(image_processor.size    ,		{'shortest_edge': 18, 'longest_edge': 13_33}    )
    self.assertEqual(image_processor.do_pad    ,		a    )

    A__       =     self.image_processing_class.from_dict(
        self.image_processor_dict    ,		size=42    ,		max_size=84    ,		pad_and_return_pixel_mask=a    )
    self.assertEqual(image_processor.size    ,		{'shortest_edge': 42, 'longest_edge': 84}    )
    self.assertEqual(image_processor.do_pad    ,		a    )






 def 						_UpperCamelCase (self       :  Dict    )       ->   Tuple:






    """simple docstring"""


    pass






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Any:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random PIL images
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		Image.Image    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values

    A__  ,			A__       =     self.image_processor_tester.get_expected_values(a    )

    self.assertEqual(
        encoded_images.shape    ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,		)

    # Test batched
    A__  ,			A__       =     self.image_processor_tester.get_expected_values(a    ,		batched=a    )

    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )    ,		)






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Dict:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random numpy tensors
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    ,		numpify=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		np.ndarray    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values

    A__  ,			A__       =     self.image_processor_tester.get_expected_values(a    )

    self.assertEqual(
        encoded_images.shape    ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values

    A__  ,			A__       =     self.image_processor_tester.get_expected_values(a    ,		batched=a    )

    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )    ,		)






 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     self.image_processing_class(**self.image_processor_dict    )
    # create random PyTorch tensors
    A__       =     prepare_image_inputs(self.image_processor_tester    ,		equal_resolution=a    ,		torchify=a    )
    for image in image_inputs:
       self.assertIsInstance(a    ,		torch.Tensor    )

    # Test not batched input
    A__       =     image_processing(image_inputs[0]    ,		return_tensors='pt'    ).pixel_values

    A__  ,			A__       =     self.image_processor_tester.get_expected_values(a    )

    self.assertEqual(
        encoded_images.shape    ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,		)

    # Test batched
    A__       =     image_processing(a    ,		return_tensors='pt'    ).pixel_values

    A__  ,			A__       =     self.image_processor_tester.get_expected_values(a    ,		batched=a    )

    self.assertEqual(
        encoded_images.shape    ,		(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            expected_height,
            expected_width,
        )    ,		)






 @slow
 def 						_UpperCamelCase (self       :  str    )       ->   str:






    """simple docstring"""


    A__       =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
    with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,		'r'    ) as f:
       A__       =     json.loads(f.read()    )

    A__       =     {'image_id': 3_97_69, 'annotations': target}

    # encode them
    A__       =     DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'    )
    A__       =     image_processing(images=a    ,		annotations=a    ,		return_tensors='pt'    )

    # verify pixel values
    A__       =     torch.Size([1, 3, 8_00, 10_66]    )
    self.assertEqual(encoding['pixel_values'].shape    ,		a    )

    A__       =     torch.tensor([0.2796, 0.3138, 0.3481]    )
    self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,		a    ,		atol=1e-4    )    )

    # verify area
    A__       =     torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,		a    )    )
    # verify boxes
    A__       =     torch.Size([6, 4]    )
    self.assertEqual(encoding['labels'][0]['boxes'].shape    ,		a    )
    A__       =     torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,		a    ,		atol=1e-3    )    )
    # verify image_id
    A__       =     torch.tensor([3_97_69]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,		a    )    )
    # verify is_crowd
    A__       =     torch.tensor([0, 0, 0, 0, 0, 0]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,		a    )    )
    # verify class_labels
    A__       =     torch.tensor([75, 75, 63, 65, 17, 17]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,		a    )    )
    # verify orig_size
    A__       =     torch.tensor([4_80, 6_40]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,		a    )    )
    # verify size
    A__       =     torch.tensor([8_00, 10_66]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,		a    )    )






 @slow
 def 						_UpperCamelCase (self       :  Union[str, Any]    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
    with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,		'r'    ) as f:
       A__       =     json.loads(f.read()    )

    A__       =     {'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

    A__       =     pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'    )

    # encode them
    A__       =     DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'    )
    A__       =     image_processing(images=a    ,		annotations=a    ,		masks_path=a    ,		return_tensors='pt'    )

    # verify pixel values
    A__       =     torch.Size([1, 3, 8_00, 10_66]    )
    self.assertEqual(encoding['pixel_values'].shape    ,		a    )

    A__       =     torch.tensor([0.2796, 0.3138, 0.3481]    )
    self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,		a    ,		atol=1e-4    )    )

    # verify area
    A__       =     torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,		a    )    )
    # verify boxes
    A__       =     torch.Size([6, 4]    )
    self.assertEqual(encoding['labels'][0]['boxes'].shape    ,		a    )
    A__       =     torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,		a    ,		atol=1e-3    )    )
    # verify image_id
    A__       =     torch.tensor([3_97_69]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,		a    )    )
    # verify is_crowd
    A__       =     torch.tensor([0, 0, 0, 0, 0, 0]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,		a    )    )
    # verify class_labels
    A__       =     torch.tensor([17, 17, 63, 75, 75, 93]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,		a    )    )
    # verify masks
    A__       =     82_28_73
    self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,		a    )
    # verify orig_size
    A__       =     torch.tensor([4_80, 6_40]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,		a    )    )
    # verify size
    A__       =     torch.tensor([8_00, 10_66]    )
    self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,		a    )    )








'''simple docstring'''







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     0
   while num > 0:
      digit_sum += num % 10
      num //= 10
   return digit_sum







def 						_A      ( UpperCAmelCase = 100						):






   '''simple docstring'''







   A__       =     1
   A__       =     2

   for i in range(2      ,max_n + 1						):
      A__       =     pre_numerator
      A__       =     2 * i // 3 if i % 3 == 0 else 1
      A__       =     cur_numerator
      A__       =     e_cont * pre_numerator + temp

   return sum_digits(UpperCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')


